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ABSTRACT
The paper studies the role of path-dependence in the resilience of EU regions. Particularly, employing a nonparametric analysis, the paper demonstrates that historical adjustments of EU regions materialized by productivity improvements, primarily in the manufacturing sector and incidentally in the sectors of construction, financial and non-market services, during the period 1995–2008 secured high(er) levels of regional resilience during the economic crisis period 2008–2013. Such a finding provides implications not only for theory but also for policy. Policies aiming at boosting regional productivity and competitiveness, which through a positive regional performance of high growth rates is concealed, a well-structured and robust production restructuring, might affect regional resilience in a way that shields regional economies not only from current imbalances but also from any future downturns.
Acknowledgements
The authors are extremely grateful to Prof. Cooke for his speedy and diligent handling of the paper, as well as to the anonymous referees for their constructive comments to the earlier version of the manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 Grifell-Tatjé and Lovell (Citation1995) suggest that the Malmquist productivity index under the CRS assumption can avoid measurement problems associated with the assumption of variable returns to scale (VRS). For a comprehensive review of different decompositions of the Malmquist productivity index see Lovell (Citation2003).
2 Also, from a technical point of view, and as has been indicated by Zelenyuk and Zheka (Citation2006), the CRS assumption allows for having a greater discriminative power compared to the VRS assumption.
3 The paper uses a simple aggregated production function in order to minimize the limitations associated with the well-known dimensionality problem involved in the nonparametric estimation of the distance functions. All the variables have been extracted from European Regional Database (Cambridge Econometrics).
4 Analogously, a distance function in relation to the technology at time t + 1 can be defined.
5 When applying DEA, the researcher does not need to pre-specify the functional form of regions’ production process. Moreover, the estimated efficiencies are relative and are based on the sample in hand.
6 That is at the sectors that sufficient data are provided; agriculture, industry, construction, trade/transportation/communication/accommodation, financial and non-market services.
7 The selection of the year 2008 (instead of the time period 1995–2008) was made due to the absence of integrated time series data.
8 On the contrary, in the case the role of productivity improvements in the whole secondary sector in regional resilience is examined, it is proved to be of inferior significance due to the cumulatively weak role of the construction sector. This fact is in line with studies that mention about negative productivity changes in the construction sector in the EU during the period under study due to the supported low-skilled immigrant flows (European Commission, Citation2009).
9 In the case, the present econometric analysis is applied in the three aggregated sectors (primary, secondary, tertiary), the primary sector receives important statistical significance in influencing regional resilience through a positive correlation between efficiency changes at the period 2001–2008 and regional growth. This fact may signify the positive contribution of the CAP decoupling reforms not only in reducing productivity gaps and supporting regional cohesion (Ezcurra, Iráizoz, Pascual, & Rapún, Citation2010) but also in the future survival of regions during major shocks.
10 The same holds if the variable of the level of specialization for each sector for the year 2008 is included in the model.
11 Similar positive and statistically significant results are provided by the inclusion of the population size of regions in the model.